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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Robust Hierarchical Dispatch Scheme for Active Distribution Networks Considering Home Thermal Flexibility

Distribution networks are changing from passive absorbers of electric energy to active distribution networks (ADNs) capable of operating and participating in electricity markets. In the context of residential microgrids, which is a type of ADNs, aggregated home heating, ventilation and air-conditioning (HVAC) loads present a key opportunity to drive operational and economic objectives, facilitate high renewable energy penetration, and enhance both system resiliency and flexibility. A robust, hierarchical dispatch scheme is developed and presented in this paper, which connects an upper level multi-phase distribution optimal power flow (DOPF) to a lower level model predictive control (MPC)-based HVAC fleet controller. The approach is tested and verified on a modified IEEE 13 bus system in an intraday market application. The results demonstrate that the proposed hierarchical dispatch scheme is able to drive both economic and operational objectives for the ADN operator.

Rooks, Cody D.↗
DOE OSTI2018

Modeling Three-Terminal III-V/Si Tandem Solar Cells

Three-terminal (3T) tandem cells fabricated by combining an interdigitated back contact (IBC) Si device with a wider bandgap top cell have the potential to provide a robust operating mechanism to efficiently capture the solar spectrum without the need to current match sub-cells or fabricate complicated metal interconnects between cells. Here we develop a two dimensional device physics model to study the behavior of IBC Si solar cells operated in a 3T configuration. We investigate how different cell designs impact device performance and discuss the analysis protocol used to understand and optimize power produced from a single junction, 3T device.

14 SOLAR ENERGY↗
DOE OSTI2024

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

Linear approximation commonly used in solving alternating-current optimal power flow (AC-OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violation. In this paper, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4,518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.

approximation algorithms↗
DOE OSTI2019

Deliverable Flexible Ramping Products Considering Spatiotemporal Correlation of Wind Generation and Demand Uncertainties

Flexible ramping products (FRPs) have been implemented by several independent system operators (ISOs) to procure adequate flexible resources. Currently, system operators estimate the system level ramping requirements ignoring the spatiotemporal correlations among various uncertainty sources. This leads to overestimates/underestimates of ramping requirements. In addition, the explicit FRPs model considers only the generation ramping limitation. Other security constraints, such as the transmission limits, are not considered, which leads to deliverability issues of FRPs. To deal with these shortcomings of current FRPs models, this paper proposes a deliverable FRPs based on a distributionally-robust chance constrained multi-interval optimal power flow (DRCC-MIOPF) considering the spatiotemporal correlation of wind power and demand uncertainties endogenously. Furthermore, an asymmetrical affine policy (AAP) is proposed to leverage generation flexibility and mitigate the uncertainty in different directions. The pricing mechanism of the FRPs is proposed using a novel uncertainty-contained locational marginal price (U_LMP) which is derived from the proposed AAP-DRCC-MIOPF model. New components representing the price of FRPs are added into the traditional LMP formulation. Finally, the PJM 5-bus, IEEE 39-bus and IEEE 118-bus systems case studies validate the proposed FRPs approach. The payment of uncertain demand and wind power on FRPs are analyzed.

electricity market↗
DOE OSTI2024

Analytic Neural Network Gaussian Process Enabled Chance-Constrained Voltage Regulation for Active Distribution Systems with PVs, Batteries and EVs

This paper proposes an analytic neural network Gaussian process (NNGP)-based chance-constrained real-time voltage regulation method for active distribution systems with photovoltaics (PVs), batteries, and electric vehicles (EVs). NNGP can utilize historical measurement data to achieve real-time probabilistic node voltage estimation through Bayesian inference. Then, NNGP is fully analytically embedded into the optimal power flow model to perform voltage regulation and adapt to various topological changes. The uncertainties of voltage estimations are easily considered via the chance constraint, and it has been shown that the adoption of this chance constraint can significantly improve the reliability of voltage regulation under various scenarios. The comparison results with other methods, carried out on a real 759-node distribution system located in western Colorado, U.S., show that the proposed method can achieve accurate voltage estimation across different topologies and reliably perform voltage regulation considering PVs, batteries, and EVs.

active distribution systems↗
DOE OSTI2025

Data-Driven Mean-Corrected Recursive Estimation-Based Optimal DER Dispatch for Distribution System Voltage Control

Recent advances in smart inverters offer opportunities to mitigate adverse grid impacts caused by high penetrations of distributed photovoltaics (PV) in distribution grids, such as voltage violations. Here, this paper proposes a novel measurement-driven optimal power flow (OPF)-based distributed energy resource management system (DERMS) voltage regulation via recursive sensitivity estimation informed coordinated control of distributed PV inverters. The proposed approach leverages available grid and controllable DER measurements, eliminating reliance on system model information while being adaptive and robust to volatile operating conditions. A mean-corrected recursive ridge regression (MCRRR) algorithm is proposed for sensitivity estimation, continuously refining the sensitivity model through a closed-form solution. It effectively manages varying grid operating conditions, such as changes in power injections and topology reconfiguration, to facilitate a time-varying update of the Load Sensitivity Factors (LSF). The proposed approach is formulated as a linear programming (LP) problem and is thus scalable to larger-scale distribution systems. Its effectiveness and efficiency are demonstrated on a realistic distribution feeder with high PV penetrations in Southern California, USA.

14 SOLAR ENERGY↗
DOE OSTI2022

Precoder Design for Physical-Layer Security and Authentication in Massive MIMO UAV Communications

Supporting reliable and seamless wireless connectivity for unmanned aerial vehicles (UAVs) has recently become a critical requirement to enable various different use cases of UAVs. Due to their widespread deployment footprint, cellular networks can support beyond visual line of sight (BVLOS) communications for UAVs. In this paper, we consider cellular connected UAVs (C-UAVs) that are served by massive multiple input-multiple-output (MIMO) links to extend coverage range, while also improving physical layer security and authentication. Here, we consider Rician channel and propose a novel linear precoder design for transmitting data and artificial noise (AN). We derive the closed-form expression of the ergodic secrecy rate of CUAVs for both conventional and proposed precoder designs. In addition, we obtain the optimal power splitting factor that divides the power between data and AN by asymptotic analysis. Then, we apply the proposed precoder design in the fingerprint embedding authentication framework, where the goal is to minimize the probability of detection of the authentication tag at an eavesdropper. In simulation results, we show the superiority of the proposed precoder in both secrecy rate and the authentication probability considering moderate and large number of antenna massive MIMO scenarios.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients

Purpose: Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Methods: Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Results: Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90% of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50% to 80% in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. Conclusions: An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs required for thoracic radiation therapy has been presented and clinically evaluated. The introduction of the proposed system in clinical routine may offer valuable new option to radiation oncologists in performing RTP.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs

Currently, there is a lack of availability of low cost, low power neuromorphic hardware. In this work, we introduce the μCaspian architecture along with an associated development PCB design which provides a low cost and SWaP (size, weight, and power) optimized neuromorphic hardware platform. Further, our proposed system only uses commercial off the shelf components and an open source FPGA workflow to maximize the accessibility of μCaspian to all researchers.

Mitchell, Parker↗
DOE OSTI2019

Egret v. 0.1 (beta)

Egret is a package that provides tools for building electric grid optimization models. This software includes objects and functions for parsing, storing, and managing electric grid data. Egret also includes the following electric grid models: - ACOPF (alternating current optimal power flow) - DCOPF – a linearized transmission model - ACOPF relaxations - Unit commitment - Economic dispatch

Knueven, Bernard↗
DOE OSTI2022

HyKKT

HyKKT (pronounced as "hiked") is a package for solving systems of linear equations of Karush-Kuhn-Tucker (KKT) form, which typically arise in optimization problems, such as optimal power flow analysis. HyKKT uses Cholesky instead of LDL^T factorization and solves the general KKT system to a desired numerical precision via block reduction and conjugate gradient on the Schur complement. Such implementation is more suitable for implementation on graphic processing units (GPUs).

Regev, Shaked↗
DOE OSTI2019

Grid Optimization Lawrence Livermore Nonlinear Programming

Modelling and solution library for security-constrained AC optimal power flow problems that uses nonlinear programming methods (interior-point methods) and solvers (Ipopt, HiOp). The code is written in C++ and provides parallel computing capabilities in the form of task parallelism (solving contingency problems in parallel). Code can run on a variety of commodity high-performance computing.

Petra, CosminG↗
DOE OSTI2013

Optimal Transmission Switching Using the IV-ACOPF Linearization

In this paper we seek to investigate the performance of transmission switching using the iterative linear program approximation to the Current Voltage AC Optimal Power Flow (IV-ACOPF). Several different methods of using this switching are investigated to find a method that addresses the MIP challenges and is both fast and accurate. We consider opening only one line, opening up to five lines, and progressively opening one line at a time. The linear method with switching is much faster than the nonlinear ACOPF and generally finds solutions within 1% of the nonlinear ACOPF.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Artificial Retina Project: Electromagnetic and Thermal Effects

This award supported the investigation on electromagnetic and thermal effects associated with the artificial retina, designed in collaboration with national laboratories, universities, and private companies. Our work over the two years of support under this award has focused mainly on 1) Design of new telemetry coils for optimal power and data transfer between the implant and the external device while achieving a significant size reduction with respect to currently used coils; 2) feasibility study of the virtual electrode configuration 3) study the effect of pulse shape and duration on the stimulation efficacy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Manufacturing of Low Cost, Durable Membrane Electrode Assemblies Engineered for Rapid Conditioning

Over the past 20 years significant progress in membrane-electrode assembly (MEA) technology development for polymer electrolyte fuel cells (PEMFCs) has resulted in the PEMFC technology approaching a commercial reality for transportation applications. However, there remain two primary technical challenges to be addressed in the MEA. First and foremost is meeting the automotive cost targets: Producing a fuel cell stack cost competitive with today’s internal combustion engine. In addition to the material cost, MEA (and other components) and stack assembly production methods must be amenable for use in low cost, high speed, automotive assembly line. One impediment to this latter goal is that stack components must currently go through a long and tedious conditioning procedure before they produce optimal power. This so-called “break-in” can take many hours, and can involve quite complex voltage, temperature and/or pressure steps. These break-in procedures must be simplified and the time required reduced if fuel cells are to become a viable automotive engine. The second challenge is to achieve the durability targets in real-world automotive duty cycle operations. Significant improvements in cost, break-in time, and durability for the key component of fuel cell stacks, MEAs were achieved in this project. Advanced modeling was used to guide design of the new MEA to maximize performance and durability. A new, innovative process and manufacturing approach utilizing direct in-line coating using scalable, cost-competitive, continuous high volume 3-layer rolled-good manufacturing processes was developed and validated by single cell and short stack testing. In addition, the direct coating methods employed were shown to reduce the cost for sacrificial films. Furthermore, Gore has demonstrated a 10 µm reinforced membrane that is used in the new low-cost process and can meet automotive power density and durability targets. Across a wide range of operating conditions, the direct-coated MEA outperformed the commercial baseline MEA, and did so through a process that delivers MEAs at $92.35/m2 at a volume of 500,000 systems per year, according to Strategic Analysis (SA) estimates.

08 HYDROGEN↗
DOE OSTI2018

SAM Photovoltaic Model Technical Reference 2016 Update

This manual describes the photovoltaic performance model in the System Advisor Model (SAM) software, Version 2016.3.14 Revision 4 (SSC Version 160). It is an update to the 2015 edition of the manual, which describes the photovoltaic model in SAM 2015.1.30 (SSC 41). This new edition includes corrections of errors in the 2015 edition and descriptions of new features introduced in SAM 2016.3.14, including: 3D shade calculator Battery storage model DC power optimizer loss inputs Snow loss model Plane-of-array irradiance input from weather file option Support for sub-hourly simulations Self-shading works with all four subarrays, and uses same algorithm for fixed arrays and one-axis tracking Linear self-shading algorithm for thin-film modules Loss percentages replace derate factors. The photovoltaic performance model is one of the modules in the SAM Simulation Core (SSC), which is part of both SAM and the SAM SDK. SAM is a user-friedly desktop application for analysis of renewable energy projects. The SAM SDK (Software Development Kit) is for developers writing their own renewable energy analysis software based on SSC. This manual is written for users of both SAM and the SAM SDK wanting to learn more about the details of SAM's photovoltaic model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Proactive Data Containers for Scientific Storage (Final Report)

Emerging HPC systems are expected to be deployed with an unprecedented level of complexity, due to a deep system memory/storage hierarchy and heterogeneity of the storage hardware. This hierarchy is expected to range from CPU cache through several levels of volatile memory to nonvolatile memory, traditional hard disks, and tape. Simple and efficient methods of data management and movement through this hierarchy is critical for scientific applications using exascale systems. Existing storage system and I/O (SSIO) technologies face severe challenges in dealing with these requirements. POSIX and MPI I/O standards that are the basis for existing I/O libraries and parallel file systems present fundamental challenges in the areas of scalable metadata operations, semantics-based data movement performance tuning, asynchronous operation, and support for scalable consistency of distributed operations. Moving toward new paradigms for SSIO in the extreme-scale era, we have proposed to investigate novel object-based data abstractions and storage mechanisms that take advantage of the deep storage hierarchy and enable proactive automated performance tuning. In order to achieve these overarching goals, we initiated an effort to develop a fundamental new data abstraction, called Proactive Data Containers (PDC). A PDC is a container within a locus of storage (memory, NVRAM, disk, etc.) that stores science data in an object-centric manner. Managing data as objects enables powerful optimization opportunities for data movement and transformations. The R&D focus of this project are: 1) formulation of object-oriented PDCs and their mapping in different levels of the exascale storage hierarchy; 2) efficient strategies for moving data in deep storage hierarchies using PDCs; 3) techniques for transforming and reorganizing data based on application requirements; and 4) novel analysis paradigms for enabling data transformations and user-defined analysis on data in PDCs. Toward achieving these overarching goals, we designed an object-centric application programing interface (API) for HPC, scalable metadata management for object-centric storage systems, and data movement optimizations such as Data Elevator for moving data between two levels of storage devices and TAPIOCA for efficient aggregation of data on compute nodes. We then implemented several components of the PDC system. They include metadata management, data placement services, remote procedure calls, data aggregation, etc. We have put them together into the overall PDC framework.

97 MATHEMATICS AND COMPUTING↗